Šiame darbe analizuotos įvairių laiko eilučių išlyginimo ir panašumo nustatymo metodų pritaikymo galimybės T bangų kaitos (TBK) analizėje, pagerinant Nearing ir Verrier pasiūlytą, modifikuoto slenkančio vidurkio metodą. Pasinaudojant TWA duomenų baze ir generuotais duomenimis surasti labiausiai TBK analizei tinkami išlyginimo ir panašumo vertinimo metodai. TBK, paskaičiuoto naudojant modifikuotą slenkančio vidurkio metodą papildytą TBK analizei tinkamiausiais laiko eilučių išlyginimo ir panašumo nustatymo metodais, tinkamumas širdies ligų diagnostikai patikrintas su duomenimis iš PTB duomenų bazės. Pasiremiant PTB duomenimis rastas galimas biomarkeris širdies ligų diagnostikoje, paskutinių dviejų TBK įverčių, gautų taikant MSVM su atviros pradžios ir pabaigos dinaminio laiko skalės kraipymo su asimetriniu judėjimo šablonu išlyginimą ir panašumą vertinant kaip absoliutinį skirtumą tarp maksimumo taškų, min-max kombinaciją. / T wave alternans (TWA) is a beat-to-beat change in the amplitude or shape of T wave. TWA is one of potential biomarkers for ventricular arrhythmias and can be a sign of serious heart disease. Because there is no gold standard in TWA measuring, modifications of existing methods and new solutions are possible. Modified moving average method, proposed by Nearing and Verrier, is one of mostly used in medical practise, but can give misleading results then T waves is not properly aligned or T wave length and morphology changes because of heart rate variability. It is known, that some ventricular arrhythmias can cause heart rate variability, so this type of error is unwanted, because online TWA measuring can become one of sudden ventricular arrhythmias predictors in the near future. In this work, variuos time series alignment and similarity detection techniques were used to improve TWA measuring and this measure capabilities in heart disease diagnostic were analized. TWA analysis with simulated and real data from ECG databases was performed and potentional biomarker was found by using biomarkers combining method, proposed by Liu, Liu and Halabi. 57.

With the proliferation of mobile devices and wireless connectivity technologies, mobile social communities offer novel opportunities for targeted marketing by service or product providers. Unfortunately, marketers are still unable to realize the full potential of these markets due to their inability to effectively target right audiences. This thesis presents a novel middleware for identifying spontaneous social communities (SSCs) of mobile users in ad hoc networks in order to facilitate marketers' advertisements. The contributions of the presented work are two fold; the first is a novel model for SSCs that captures their unique dynamic nature, in terms of community structure and interest in different \textit{hot-topics} over time. These time-varying interests are represented through an inferred \textit{community profile prototype} that reflects dominant characteristics of community members. This prototype is then employed to facilitate the identification of new potential members. The selected community prototypes are also used by marketers to identify the right communities for their services or products promotions. The second contribution of this paper is novel distributed techniques for efficient calculation of the community prototypes and identification of potential community links. In contrast to traditional models of detecting fixed and mobile social networks that rely on pre-existing friendships among its members to predict new ones, the proposed model focuses on measuring the degree of similarity between the new user's profile and the profiles of members of each community in order to predict new users' relationships in the community. The adopted model of SSCs can foster many existing and new socially-aware applications such as recommender systems for social events and tools for collaborative work. It is also an ideal target for business-oriented applications such as short-message-service (SMS) advertisement messages, podcasting news feeds in addition to location/context-aware services. The performance of the proposed work was evaluated using the NetLogo platform where obtained experimental results demonstrate the achieved high degree of stability in the resulting communities in addition to the effectiveness of the proposed middleware in terms of the reduction in the number of routing messages required for advertisements.

This thesis considers possible criteria for the selection of example sentences for difficult or unknown words in reading texts for students of German as a Second Language (GSL). The examples are intended to be provided within the context of an Intelligent Computer-Aided Language Learning (ICALL) Vocabulary Learning System, where students can choose among several explanation options for difficult words. Some of these options (e.g. glosses) have received a good deal of attention in the ICALL/Second Language (L2) Acquisition literature; in contrast, literature on examples has been the near exclusive province of lexicographers. The selection of examples is explored from an educational, L2 teaching point of view: the thesis is intended as a first exploration of the question of what makes an example helpful to the L2 student from the perspective of L2 teachers. An important motivation for this work is that selecting examples from a dictionary or randomly from a corpus has several drawbacks: first, the number of available dictionary examples is limited; second, the examples fail to take into account the context in which the word was encountered; and third, the rationale and precise principles behind the selection of dictionary examples is usually less than clear. Central to this thesis is the hypothesis that a random selection of example sentences from a suitable corpus can be improved by a guided selection process that takes into account characteristics of helpful examples. This is investigated by an empirical study conducted with teachers of L2 German. The teacher data show that four dimensions are significant criteria amenable to analysis: (a) reduced syntactic complexity, (b) sentence similarity, provision of (c) significant co-occurrences and (d) semantically related words. Models based on these dimensions are developed using logistic regression analysis, and evaluated through two further empirical studies with teachers and students of L2 German. The results of the teacher evaluation are encouraging: for the teacher evaluation, they indicate that, for one of the models, the top-ranked selections perform on the same level as dictionary examples. In addition, the model provides a ranking of potential examples that roughly corresponds to that of experienced teachers of L2 German. The student evaluation confirms and notably improves on the teacher evaluation in that the best-performing model of the teacher evaluation significantly outperforms both random corpus selections and dictionary examples (when a penalty for missing entries is included).

L’objectif de ces travaux est d’étudier expérimentalement la dispersion d’hydrogène pour évaluer l’impact des fuites chroniques ou accidentelles qui peuvent intervenir en milieu libre ou encombré, à faible ou à forte pression, sur un véhicule fonctionnant avec une pile à combustible. Les fuites étudiées sont assimilées à des jets verticaux, turbulents, axisymétriques, à densité variable, et issus d’orifices cylindriques de 1 à 3 mm de diamètre. Un banc expérimental a été conçu pour étudier ces fuites : l’hydrogène a été remplacé par de l’hélium pour des raisons de sécurité. Il résiste à une pression de 200 bars et permet de positionner un obstacle dans le jet. La technique BOS (Background Oriented Schlieren) a été adaptée aux jets millimétriques et à la présence d’un obstacle. Un soin particulier à été apporté à la mise en place de cette technique. Les résultats obtenus sont en accord avec ceux de la littérature quand il en existe. De nouvelles lois de similitude sont proposées, plus représentatives de la physique des jets : libres subsoniques, libres sousdétendus, et subsoniques en présence d’un obstacle (sphère). À partir de l’analyse de la structure compressible des jets sous-détendus, de nouvelles lois sont aussi proposées pour estimer la position et le diamètre du disque de Mach puis la longueur du cône potentiel. Enfin, deux lois d’estimation du volume et de la masse inflammables de jets libres d’hydrogène sont proposées : elles sont fonction du débit massique de la fuite. / The aim of this work is to experimentally investigate the hydrogen dispersion to evaluate the impact of chronic or accidental leaks that may occur in a free or in a congested environment, from a low or a high pressure tank, on a fuel cell vehicle. The leaks are assimilated to vertical turbulent and axisymmetric jets with variable density. They are issued from cylindrical orifices from 1 to 3 mm diameter. An experimental set-up was designed to investigate the leaks : hydrogen has been replaced by helium for safety reasons. It supports a 200 bar pressure and allows to position an obstacle in the jet flow. The BOS (Background Oriented Schlieren) technique has been adapted to millimeter jets and to the presence of an obstacle. Particular attention has been given to the development of this technique. The results show good agrement with the available litterature data.New similarity laws are proposed, more representative of the flows of : subsonic free jets, under-expanded free jets, and subsonic impinging (a sphere) jets. From the structure analysis of compressible under-expanded free jets, new relations are proposed to estimate the Mach disk position and diameter as well as the potential core length. Finally, two new laws are found from the analysis of flammable volume and flammable mass : they are based on the leak mass flow rate.

Process model matchers automate the detection of activities that represent similar functionality in different models. Thus, they provide support for various tasks related to the management of business processes including model collection management and process design. Yet, prior research primarily demonstrated the matchers’ effectiveness, i.e., the accuracy and the completeness of the results. In this context (i) the size of the empirical data is often small, (ii) all data is used for the matcher development, and (iii) the validity of the design decisions is not studied. As a result, existing matchers yield a varying and typically low effectiveness when applied to different datasets, as among others demonstrated by the process model matching contests in 2013 and 2015. With this in mind, the thesis studies the effectiveness of matchers by separating development from evaluation data and by empirically analyzing the validity and the limitations of design decisions. In particular, the thesis develops matchers that rely on different sources of information. First, the activity labels are considered as natural-language descriptions and the Bag-of-Words Technique is introduced which achieves a high effectiveness in comparison to the state of the art. Second, the Order Preserving Bag-of-Words Technique analyzes temporal dependencies between activities in order to automatically configure the Bag-of-Words Technique and to improve its effectiveness. Third, expert feedback is used to adapt the matchers to the domain characteristics of process model collections. Here, the Adaptive Bag-of-Words Technique is introduced which outperforms the state-of-the-art matchers and the other matchers from this thesis.

The rapid increase in mobile malware and deployment of over-privileged applications over the years has been of great concern to the security community. Encroaching on user’s privacy, mobile applications (apps) increasingly exploit various sensitive data on mobile devices. The information gathered by these applications is sufficient to uniquely and accurately profile users and can cause tremendous personal and financial damage. On Android specifically, the security and privacy holes in the operating system and framework code has created a whole new dynamic for malware and privacy exploitation. This research work seeks to develop novel analysis techniques that monitor Android applications for possible unwanted behaviors and then suggest various ways to deal with the privacy leaks associated with them. Current state-of-the-art static malware analysis techniques on Android-focused mainly on detecting known variants without factoring any kind of software obfuscation. The dynamic analysis systems, on the other hand, are heavily dependent on extending the Android OS and/or runtime virtual machine. These methodologies often tied the system to a single Android version and/or kernel making it very difficult to port to a new device. In privacy, accesses to the database system’s objects are not controlled by any security check beyond overly-broad read/write permissions. This flawed model exposes the database contents to abuse by privacy-agnostic apps and malware. This research addresses the problems above in three ways. First, we developed a novel static analysis technique that fingerprints known malware based on three-level similarity matching. It scores similarity as a function of normalized opcode sequences found in sensitive functional modules and application permission requests. Our system has an improved detection ratio over current research tools and top COTS anti-virus products while maintaining a high level of resiliency to both simple and complex obfuscation. Next, we augment the signature-related weaknesses of our static classifier with a hybrid analysis system which incorporates bytecode instrumentation and dynamic runtime monitoring to examine unknown malware samples. Using the concept of Aspect-oriented programming, this technique involves recompiling security checking code into an unknown binary for data flow analysis, resource abuse tracing, and analytics of other suspicious behaviors. Our system logs all the intercepted activities dynamically at runtime without the need for building custom kernels. Finally, we designed a user-level privacy policy enforcement system that gives users more control over their personal data saved in the SQLite database. Using bytecode weaving for query re-writing and enforcing access control, our system forces new policies at the schema, column, and entity levels of databases without rooting or voiding device warranty.

Human fingerprints are rich in details called the minutiae, which can be used as identification marks for fingerprint verification. To get the details, the fingerprint capturing techniques are to be improved. Since when we the fingerprint is captured, the noise from outside adds to it. The goal of this thesis is to remove the noise present in the fingerprint image. To achieve a good quality fingerprint image, this noise has to be removed or suppressed and here it is done by using an algorithm or technique called ’Spectral Subtraction’, where the algorithm is based on subtraction of estimated noise spectrum from noisy signal spectrum. The performance of the algorithm is assessed by comparing the original fingerprint image and image obtained after spectral subtraction several parameters like PSNR, SSIM and also for different fingerprints on the database. Finally, performance matching was done using NIST matching software, and the obtained results were presented in the form of Receiver Operating Characteristics (ROC)graphs, using MATLAB, and the experimental results were presented.

The screening of chemical libraries is an important step in the drug discovery process. The existing chemical libraries contain up to millions of compounds. As the screening at such scale is expensive, the virtual screening is often utilized. There exist several variants of virtual screening and ligand- based virtual screening is one of them. It utilizes the similarity of screened chemical compounds to known compounds. Besides the employed similarity measure, another aspect greatly influencing the performance of ligand-based virtual screening is the chosen chemical compound representation. In this thesis, we introduce a fragment-based representation of chemical compounds. Our representation utilizes fragments to represent a compound. Each fragment is represented by its physicochemical descriptors. The representation is highly parameterizable, especially in the area of physicochemical descriptors selection and application. In order to test the performance of our method, we utilized an existing framework for virtual screening benchmarking. The results show that our method is comparable to the best existing approaches and on some datasets it outperforms them.

The main subject of this thesis is to design a method that is able to compare the outputs of a manually set up particle system with a video template of a simulated particle phenomenon. For the sake of simplicity, this work is focused exclusively to smoke effects and its comparisons. Specifically, descriptors depicting the color scheme of the examined input videos are proposed as well as the shape descriptors and time variability descriptors, which are based on the image segmentation method called Alpha Matting. This method is able to distinguish foreground objects from the background, while the foreground objects are described together with their transparency. Video template recordings captured specially for the purpose of this work are included. At the end, the resulting method is supported by an example of the gradually improving smoke effect simulation. Powered by TCPDF (www.tcpdf.org)

This work is focusing on exploration of multimedia collections. It describes the problematic of exploration and proposes new approaches to it, two based on the data structure M-Index and two utilizing multiple similarity models at once. Those approaches were compared using an extensive user study. Part of this work is also devoted to analysis of a new exploration system, design of its architecture, system implementation and its deployment. This exploration system was used in several applications, which are also shown and described in this thesis.